Journal article
A Diffusion Network Event History Estimator
The Journal of politics, Vol.85(2), pp.436-452
04/2023
DOI: 10.1086/723804
Abstract
Research on the diffusion of political decisions across jurisdictions typically accounts for units’influence over each other with (1) observable measures and/or (2) by inferring latent networkties from past decisions. The former approach assumes that interdependence is static andperfectly captured by the data. The latter mitigates these issues, but requires analytical toolsthat are separate from the main empirical methods for studying diffusion. As a solution, weintroduce Network Event History Analysis (NEHA), which incorporates latent network infer-ence into conventional discrete-time event history models. We demonstrate NEHA’s uniquemethodological and substantive benefits in applications to policy adoption in the Americanstates. Researchers can analyze the ties and structure of inferred networks to refine modelspecifications, evaluate diffusion mechanisms, and/or test new or existing hypotheses. By cap-turing targeted relationships unexplained by standard covariates, NEHA can improve models,facilitate richer theoretical development, and permit novel analyses of the diffusion process.
Details
- Title: Subtitle
- A Diffusion Network Event History Estimator
- Creators
- Jeffrey J. HardenBruce A. DesmaraisMark BrockwayFrederick J. BoehmkeScott J. LaCombeFridolin LinderHanna Wallach
- Resource Type
- Journal article
- Publication Details
- The Journal of politics, Vol.85(2), pp.436-452
- DOI
- 10.1086/723804
- ISSN
- 0022-3816
- eISSN
- 1468-2508
- Language
- English
- Electronic publication date
- 01/18/2023
- Date published
- 04/2023
- Academic Unit
- Political Science; Public Policy Center (Archive)
- Record Identifier
- 9984362358702771
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